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雷达卡
摘要翻译：
结合空间正则化的光谱解混方法已经显示出越来越多的兴趣。虽然促进丰度图平滑的空间正则化器已被广泛使用，但它们可能会过度平滑这些图，特别是可能无法保留高光谱图像中存在的边缘。现有的解混方法通常忽略这些边缘结构，或者使用来自高光谱图像本身的边缘信息。然而，该信息可能受到大量噪声或光照变化的影响，导致将错误的空间信息合并到解混过程中。本文提出了一个简单而强大的光谱分解框架，它结合了外部数据（即激光雷达数据）。激光雷达测量可以很容易地用来调整应用于解混过程的标准空间正则化。利用两个模拟数据集和一幅真实的高光谱图像对所提出的框架进行了严格的评估。它与依赖于高光谱图像的空间信息的竞争方法进行了比较。结果表明，该框架能够提供更好的丰度估计，特别是对受阴影影响的像素的丰度估计有明显的改善。
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英文标题：
《Hyperspectral image unmixing with LiDAR data-aided spatial
regularization》
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作者：
Tatsumi Uezato, Mathieu Fauvel and Nicolas Dobigeon
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最新提交年份：
2017
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Physics 物理学
二级分类：Data Analysis, Statistics and Probability 数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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英文摘要：
Spectral unmixing methods incorporating spatial regularizations have demonstrated increasing interest. Although spatial regularizers which promote smoothness of the abundance maps have been widely used, they may overly smooth these maps and, in particular, may not preserve edges present in the hyperspectral image. Existing unmixing methods usually ignore these edge structures or use edge information derived from the hyperspectral image itself. However, this information may be affected by large amounts of noise or variations in illumination, leading to erroneous spatial information incorporated into the unmixing procedure. This paper proposes a simple, yet powerful, spectral unmixing framework which incorporates external data (i.e. LiDAR data). The LiDAR measurements can be easily exploited to adjust standard spatial regularizations applied to the unmixing process. The proposed framework is rigorously evaluated using two simulated datasets and a real hyperspectral image. It is compared with competing methods that rely on spatial information derived from a hyperspectral image. The results show that the proposed framework can provide better abundance estimates and, more specifically, can significantly improve the abundance estimates for pixels affected by shadows.
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PDF链接：
https://arxiv.org/pdf/1712.07862
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